Co-occurence patterns are used in eological fields of science to reveal interations between organisms. In this study, I applied this idea to explore the patterns of insect damages and diseases in which I observed in the rice farmer’s fileds across countries. We scoped thoses areas espectilly at lowland rice area. We collected incidence of damages caused by insect pests and disease symptoms.

#-- Here are the package used for analyse the survey data.
require(plyr)
## Loading required package: plyr
require(dplyr)
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## 
## Attaching package: 'dplyr'
## 
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## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
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require(reshape)
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require(reshape2)
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require(WGCNA)
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## ==========================================================================
## *
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## *
## *    Important note: It appears that your system supports multi-threading,
## *    but it is not enabled within WGCNA in R. 
## *    To allow multi-threading within WGCNA with all available cores, use 
## *
## *          allowWGCNAThreads()
## *
## *    within R. Use disableWGCNAThreads() to disable threading if necessary.
## *    Alternatively, set the following environment variable on your system:
## *
## *          ALLOW_WGCNA_THREADS=<number_of_processors>
## *
## *    for example 
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## *    To set the environment variable in linux bash shell, type 
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## *           export ALLOW_WGCNA_THREADS=4
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## *     before running R. Other operating systems or shells will
## *     have a similar command to achieve the same aim.
## *
## ==========================================================================
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require(cowplot)
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require(RColorBrewer)
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require(pheatmap)
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require(lattice)
## Loading required package: lattice

Looking at the survey data

##    dhx  whx ssx    wma    lfa   defa bphx wbpx awx rbx rbbx glhx stbx
## 1 6.07 4.14   0 327.73 124.23 398.30    0    0   0   0    0   13    0
## 2 8.27 0.53   0 339.78 181.41 342.53    1    0   0   0    0   26    0
## 3 0.91 1.96   0 118.07  76.19 250.93    4    0   0   0    0   46    0
## 4 0.91 1.54   0  68.80  22.06  71.46    4    0   0   0    0   50    0
## 5 2.36 1.72   0 439.34 898.72 526.36    1    0   0   1    0   18    0
## 6 0.80 0.48   0 482.58 625.48 400.26    1    0   0   1    0   10    0
##      blba   lba   bsa  blsa    nbsa  rsa    lsa  shbx shrx  srx fsmx  nbx
## 1    0.00  0.00  0.00 23.81 1693.29 0.00   0.00  1.63 0.00 0.67    0 7.79
## 2    0.00  0.00  0.00 66.37 1622.39 0.00   0.00  1.54 0.00 0.00    0 0.00
## 3  762.49  0.00 19.09  0.00 2212.19 0.29 156.04  2.73 0.83 1.92    0 0.38
## 4 1012.57  0.00 36.58  0.00 1930.49 0.00 127.78 10.48 0.56 0.95    0 0.00
## 5   65.63 72.48  0.00  0.00 1144.98 0.00   0.00  3.33 1.05 0.00    0 4.71
## 6    0.00 56.08  0.00 40.00 1014.03 0.00   0.00  6.67 1.58 0.00    0 1.45
##   dpx rtdx rsdx  rtx
## 1   0    0    0 5.54
## 2   0    4    0 5.72
## 3   0    8    0 0.45
## 4   0    8    0 6.43
## 5   0    0    0 2.86
## 6   0    6    0 4.84

Survey data set is composed of 420 farmer’s fileds and 29 variables.

## [1] 420  29

What did we observe? Our variables interesting are “dhx” (deadhead) “whx” (whitehead) “ssx” (silvershoot) “wma” (whorl maggot) “lfa” (leaffolder) “defa” (defoliators) “bphx” (brown plant hopper) “wbpx” (white backed plant hopper) “awx” (army worm) “rbx” (rice bug) “rbbx” (rice black bug) “glhx” (green leaf hopper) “stbx” (singk bug) “blba” (bacterial leaf blight) “lba” (leaf blast) “bsa” (brown spot) “blsa” (bacterial leaf streak) “nbsa” (narrow brown spot) “rsa” (red stripe) “lsa” (leaf scald) “shbx” (shealth blight) “shrx” (shealth rot) “srx” (stem rot) “fsmx” (false smut) “nbx” (neck blast) “dpx” (dirty panicle) “rtdx” (rice tugro disease) “rsdx” (ragged stund disease) “rtx” (rat damages)

##  [1] "dhx"  "whx"  "ssx"  "wma"  "lfa"  "defa" "bphx" "wbpx" "awx"  "rbx" 
## [11] "rbbx" "glhx" "stbx" "blba" "lba"  "bsa"  "blsa" "nbsa" "rsa"  "lsa" 
## [21] "shbx" "shrx" "srx"  "fsmx" "nbx"  "dpx"  "rtdx" "rsdx" "rtx"
m.all <- melt(all)
## Using  as id variables
varnames <- colnames(all)
i <- 1
out <- NULL
for(i in 1:length(varnames)) {
        gdata <- m.all %>% filter(variable == varnames[i])
        p <- ggplot(gdata, aes(x = value)) + 
        geom_histogram(stat = "bin") + ggtitle(paste("Histogram of", varnames[i], sep = " "))
        dev.new()
        print(p) 
        out[[i]] <- p
}
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
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grid.arrange(out[[1]],
             out[[2]],
             out[[3]],
             out[[4]],
             out[[5]],
             out[[6]],
             out[[7]],
             out[[8]],
             out[[9]],
             out[[10]],
             out[[11]],
             out[[12]],
             out[[13]],
             out[[14]],
             out[[15]],
             out[[16]],
             out[[17]],
             out[[18]],
             out[[19]],
             out[[20]],
             out[[21]],
             out[[22]],
             out[[23]],
             out[[24]],
             out[[25]],
             out[[26]],
             out[[26]],
             out[[28]],
             out[[29]],
             nrow = 5
        )
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df <- lapply(all, shapiro.test)
df
## $DH
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.4481, p-value < 2.2e-16
## 
## 
## $WH
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.47937, p-value < 2.2e-16
## 
## 
## $SS
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.35667, p-value < 2.2e-16
## 
## 
## $WM
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.67948, p-value < 2.2e-16
## 
## 
## $LF
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.7443, p-value < 2.2e-16
## 
## 
## $DEF
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.42017, p-value < 2.2e-16
## 
## 
## $BPH
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.48335, p-value < 2.2e-16
## 
## 
## $WPH
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.34616, p-value < 2.2e-16
## 
## 
## $AM
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.23188, p-value < 2.2e-16
## 
## 
## $RB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.22887, p-value < 2.2e-16
## 
## 
## $RBB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.1183, p-value < 2.2e-16
## 
## 
## $GLH
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.3914, p-value < 2.2e-16
## 
## 
## $STB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.10739, p-value < 2.2e-16
## 
## 
## $BLB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.65489, p-value < 2.2e-16
## 
## 
## $LB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.30646, p-value < 2.2e-16
## 
## 
## $BS
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.61137, p-value < 2.2e-16
## 
## 
## $BLS
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.3734, p-value < 2.2e-16
## 
## 
## $NBS
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.57307, p-value < 2.2e-16
## 
## 
## $RS
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.2169, p-value < 2.2e-16
## 
## 
## $LS
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.19553, p-value < 2.2e-16
## 
## 
## $SHB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.65975, p-value < 2.2e-16
## 
## 
## $SHR
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.22628, p-value < 2.2e-16
## 
## 
## $SR
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.15897, p-value < 2.2e-16
## 
## 
## $FSM
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.3224, p-value < 2.2e-16
## 
## 
## $NB
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.37885, p-value < 2.2e-16
## 
## 
## $DP
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.36804, p-value < 2.2e-16
## 
## 
## $RTD
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.13213, p-value < 2.2e-16
## 
## 
## $RSD
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.2422, p-value < 2.2e-16
## 
## 
## $RD
## 
##  Shapiro-Wilk normality test
## 
## data:  X[[i]]
## W = 0.2182, p-value < 2.2e-16
##### construct the correlation matrix ######

all.pearson <- cor(all, method = "pearson", use = "pairwise") # pearson correlation

all.spearman <- cor(all, method = "spearman", use = "pairwise") # spearman correlation

all.kendall <- cor(all, method = "kendall", use = "pairwise")# kendall correlation

all.biweight <- bicor(all, use = "pairwise") # Biweight Midcorrelation from WGCNA package
## Warning in bicor(all, use = "pairwise"): bicor: zero MAD in variable 'x'.
## Pearson correlation was used for individual columns with MAD=NA.
#===========================================
##### Transform the correlation matrix #####
#============================================

# change from matrix to data frame, and extract the value of each correlation approach

### Peason correlation
df.pearson <- as.data.frame(all.pearson)
df.pearson.corval <- df.pearson[1]
colnames(df.pearson.corval) <- "Pearson"

### Spearman correlation

df.spear <- as.data.frame(all.spearman)
df.spear.corval <- df.spear[1]
colnames(df.spear.corval) <- "Spearman"

### Kendall correlation

df.kendall <- as.data.frame(all.kendall)
df.kendall.corval <- df.kendall[1]
colnames(df.kendall.corval) <- "Kendall"

### Biweight Midcorrelation
df.biweight <- as.data.frame(all.biweight)
df.biweight.cor.val <- df.biweight[1]
colnames(df.biweight.cor.val) <- "Biweight"

#====================================================
##### Combine correlation value of each method ######
#===================================================
# will add more correlation
bind.cor <- cbind(df.pearson.corval,
                  df.spear.corval,
                  df.kendall.corval,
                  df.biweight.cor.val)

##### Cluster Analysis and correlation matrix #####
cor.of.cor <- cor(bind.cor)
pheatmap(cor.of.cor, cellwidth = 50, cellheight = 50, fontsize = 16)

#===================================================
# Check the distribution of the correlation of each measures
#===================================================
# data <- all
# 
# options(warnings=-1)
# 
# results <- matrix(nrow = 0, ncol = 4)
# 
# for(a in 1:length(names(all))){
#       #every species will be compared to every other species, so there has to be another loop that iterates down the rest of the columns
#       for(b in (a+1):length(names(all))){
#               
#              
#           #summing the abundances of species of the columns that will be compared
#           species1.ab <- sum(data[,a])
#           species2.ab <- sum(data[,b])
#       
#           
#           if(species1.ab > 1 & species2.ab > 1){
#               test <- cor.test(data[,a], data[,b], method = "spearman", na.action = na.rm)
#               rho <- test$estimate
#               p.value <- test$p.value
#           
#           }   
#           
#           new.row <- c(names(data)[a], names(data)[b], rho, p.value)
#           results <- rbind(results, new.row)          
#       }
# 
# }
# 
# results <- data.frame(data.matrix(results))
# 
# names(results) <- c("var1","var2","rho","p.value")
# 
# head(results)

# We do not neet the 1 in correlation matrix
remove <- 1
all.pearson
##               DH           WH           SS           WM           LF
## DH   1.000000000  0.325028179  0.060714261 -0.003873017  0.099361452
## WH   0.325028179  1.000000000  0.047215869  0.069069491  0.058922728
## SS   0.060714261  0.047215869  1.000000000  0.051112291 -0.010646041
## WM  -0.003873017  0.069069491  0.051112291  1.000000000 -0.079434869
## LF   0.099361452  0.058922728 -0.010646041 -0.079434869  1.000000000
## DEF  0.007983573  0.063957932 -0.071922778  0.338084847  0.058017833
## BPH -0.007625245 -0.016714635 -0.106112545 -0.173007667  0.028206927
## WPH  0.037662869  0.031534680 -0.095537645 -0.084193938  0.033586134
## AM   0.037495252  0.009248065 -0.005562132  0.069080735  0.098121261
## RB   0.003874718  0.019895873 -0.022765649 -0.039396393 -0.042645761
## RBB -0.002623576 -0.048915943 -0.039853861 -0.075850184 -0.024415549
## GLH  0.054328231  0.153810194 -0.093516577  0.340943978  0.002649266
## STB  0.371356516  0.421096258  0.114114403 -0.013255587 -0.052122995
## BLB  0.052831640  0.001531788  0.423141491  0.009089894 -0.053662925
## LB   0.015368450 -0.008987047  0.024874450  0.062859736  0.080515694
## BS  -0.050242734 -0.143371863  0.253292900  0.020998115 -0.026092119
## BLS -0.035817848 -0.035559916  0.328430635  0.090787441 -0.093289763
## NBS  0.184982522 -0.101752227  0.113846398  0.110176588  0.011793281
## RS  -0.054823475 -0.051567548  0.252548622  0.117181166 -0.052954078
## LS  -0.026900886 -0.077648511  0.161431420  0.081125253  0.007604723
## SHB -0.093169462  0.004342073 -0.093944326  0.003680308  0.153230602
## SHR  0.059118801  0.004126594  0.182915243  0.003529975 -0.009149067
## SR   0.005936391  0.007276051 -0.028449763 -0.033968602  0.022049287
## FSM -0.079641765 -0.035724517  0.111744464 -0.105304409 -0.026579679
## NB  -0.084438068  0.017602244 -0.086348192 -0.090234294 -0.083294692
## DP  -0.073634577 -0.120887145 -0.053961520  0.149253069 -0.054636388
## RTD  0.028011578  0.013827655 -0.045734847  0.132046528 -0.028749324
## RSD -0.024364407 -0.053436077 -0.063294985  0.208855373  0.088755004
## RD   0.111503361  0.057743983 -0.036645727  0.035474259  0.065819201
##              DEF           BPH          WPH           AM           RB
## DH   0.007983573 -0.0076252446  0.037662869  0.037495252  0.003874718
## WH   0.063957932 -0.0167146348  0.031534680  0.009248065  0.019895873
## SS  -0.071922778 -0.1061125448 -0.095537645 -0.005562132 -0.022765649
## WM   0.338084847 -0.1730076674 -0.084193938  0.069080735 -0.039396393
## LF   0.058017833  0.0282069269  0.033586134  0.098121261 -0.042645761
## DEF  1.000000000 -0.1163678129 -0.057329295 -0.037959277 -0.017016241
## BPH -0.116367813  1.0000000000  0.093677764  0.023511170 -0.019305014
## WPH -0.057329295  0.0936777643  1.000000000 -0.011865248  0.159104324
## AM  -0.037959277  0.0235111698 -0.011865248  1.000000000  0.001447928
## RB  -0.017016241 -0.0193050138  0.159104324  0.001447928  1.000000000
## RBB -0.051227130 -0.0284286328 -0.024962455 -0.031130647  0.057525855
## GLH  0.486621298 -0.0007295963 -0.010722921  0.064324325 -0.005068459
## STB -0.013529366 -0.0560481141 -0.037737864 -0.023569508  0.032348592
## BLB -0.162739286 -0.1933769531 -0.047736644 -0.072285172 -0.008495089
## LB   0.006220407 -0.0147726990  0.064962940  0.041548551  0.023719271
## BS  -0.045116622 -0.0835676113 -0.081988672  0.027655628  0.021073785
## BLS  0.080578248 -0.1402127615 -0.099999444 -0.053143273 -0.072818244
## NBS  0.175323168 -0.1421309017 -0.156151154 -0.084195536 -0.071457434
## RS  -0.067610272 -0.0911838755 -0.061054629  0.043515286 -0.042381590
## LS   0.044988743 -0.0629941966 -0.043876836 -0.035949113 -0.033424055
## SHB  0.249693636  0.0136212599  0.084802700 -0.084676095 -0.026293083
## SHR -0.022242612  0.0368470116 -0.068675537 -0.036706555 -0.040152014
## SR   0.041302031 -0.0465660547 -0.053314267 -0.021632356 -0.022372806
## FSM -0.077180634  0.2409778217  0.031664491 -0.013542181 -0.055588817
## NB  -0.061607019  0.3700817259  0.129589993 -0.020150458  0.008954225
## DP   0.034958674 -0.0487722431  0.068384328  0.280690007  0.277299100
## RTD  0.245850080  0.0247196239  0.030959741 -0.027844657  0.022468041
## RSD  0.062848757  0.1068054835  0.103988111  0.149814602  0.023745868
## RD   0.096161736  0.1025612287  0.003756174  0.250027255  0.002287739
##              RBB           GLH          STB          BLB           LB
## DH  -0.002623576  0.0543282307  0.371356516  0.052831640  0.015368450
## WH  -0.048915943  0.1538101942  0.421096258  0.001531788 -0.008987047
## SS  -0.039853861 -0.0935165770  0.114114403  0.423141491  0.024874450
## WM  -0.075850184  0.3409439780 -0.013255587  0.009089894  0.062859736
## LF  -0.024415549  0.0026492655 -0.052122995 -0.053662925  0.080515694
## DEF -0.051227130  0.4866212981 -0.013529366 -0.162739286  0.006220407
## BPH -0.028428633 -0.0007295963 -0.056048114 -0.193376953 -0.014772699
## WPH -0.024962455 -0.0107229205 -0.037737864 -0.047736644  0.064962940
## AM  -0.031130647  0.0643243251 -0.023569508 -0.072285172  0.041548551
## RB   0.057525855 -0.0050684588  0.032348592 -0.008495089  0.023719271
## RBB  1.000000000 -0.0385252862 -0.017809711 -0.021583211 -0.016640491
## GLH -0.038525286  1.0000000000  0.048136932 -0.126229122 -0.007330015
## STB -0.017809711  0.0481369316  1.000000000  0.041859222 -0.001026795
## BLB -0.021583211 -0.1262291215  0.041859222  1.000000000  0.028843892
## LB  -0.016640491 -0.0073300154 -0.001026795  0.028843892  1.000000000
## BS  -0.076681362 -0.1315080429 -0.060772908  0.391470737  0.142172028
## BLS -0.046039961 -0.0866885451 -0.017659744  0.440912637 -0.050137888
## NBS  0.035089102  0.1304563137  0.003025311  0.264444501 -0.088213146
## RS  -0.029597342 -0.0687090694 -0.026233232  0.223250059 -0.006967906
## LS  -0.015729476  0.0605400613 -0.024194676  0.291634796 -0.033583693
## SHB -0.060070162  0.0377580677 -0.046707841 -0.052898252 -0.127963391
## SHR -0.026494705 -0.0244763993 -0.021833211  0.244004867 -0.029882883
## SR   0.106665543 -0.0138986204 -0.014435547 -0.009328385 -0.038375603
## FSM -0.005931387  0.0062990502 -0.005034030  0.107005210  0.107158871
## NB  -0.004314071 -0.0425309147 -0.036022872 -0.167556950 -0.046065138
## DP  -0.045834422 -0.0769782565 -0.040783560 -0.103919379  0.190899493
## RTD -0.020833404  0.3551117043 -0.018460025 -0.044136606 -0.028528354
## RSD -0.027352867  0.1453529983 -0.028585111 -0.099718468  0.163447521
## RD  -0.026835270  0.1404807312 -0.010717476 -0.068420341  0.110923956
##              BS         BLS          NBS           RS           LS
## DH  -0.05024273 -0.03581785  0.184982522 -0.054823475 -0.026900886
## WH  -0.14337186 -0.03555992 -0.101752227 -0.051567548 -0.077648511
## SS   0.25329290  0.32843064  0.113846398  0.252548622  0.161431420
## WM   0.02099811  0.09078744  0.110176588  0.117181166  0.081125253
## LF  -0.02609212 -0.09328976  0.011793281 -0.052954078  0.007604723
## DEF -0.04511662  0.08057825  0.175323168 -0.067610272  0.044988743
## BPH -0.08356761 -0.14021276 -0.142130902 -0.091183876 -0.062994197
## WPH -0.08198867 -0.09999944 -0.156151154 -0.061054629 -0.043876836
## AM   0.02765563 -0.05314327 -0.084195536  0.043515286 -0.035949113
## RB   0.02107378 -0.07281824 -0.071457434 -0.042381590 -0.033424055
## RBB -0.07668136 -0.04603996  0.035089102 -0.029597342 -0.015729476
## GLH -0.13150804 -0.08668855  0.130456314 -0.068709069  0.060540061
## STB -0.06077291 -0.01765974  0.003025311 -0.026233232 -0.024194676
## BLB  0.39147074  0.44091264  0.264444501  0.223250059  0.291634796
## LB   0.14217203 -0.05013789 -0.088213146 -0.006967906 -0.033583693
## BS   1.00000000  0.22362745 -0.050714128  0.102515319  0.368885671
## BLS  0.22362745  1.00000000  0.152786505  0.511482195  0.135736272
## NBS -0.05071413  0.15278651  1.000000000  0.101844665  0.163722873
## RS   0.10251532  0.51148219  0.101844665  1.000000000  0.029697842
## LS   0.36888567  0.13573627  0.163722873  0.029697842  1.000000000
## SHB -0.14832111  0.01044052 -0.040997043 -0.061610845 -0.034450014
## SHR  0.23465025  0.28316518  0.062340280  0.134249332  0.067177169
## SR  -0.07294069 -0.01516131  0.110136273 -0.020632530 -0.015110894
## FSM  0.06312523  0.06480554 -0.073086344 -0.020909677  0.080208260
## NB  -0.11748518 -0.09159795 -0.132854423 -0.051119154 -0.061517557
## DP   0.36003161 -0.09823982 -0.121521398 -0.032796485 -0.027564559
## RTD -0.01530584 -0.04515759  0.154641961 -0.028498321  0.017224106
## RSD  0.11846480 -0.07102290 -0.100522766 -0.036775695  0.133074647
## RD  -0.02783377 -0.02423031 -0.014419614 -0.043732829 -0.011012836
##              SHB          SHR           SR          FSM           NB
## DH  -0.093169462  0.059118801  0.005936391 -0.079641765 -0.084438068
## WH   0.004342073  0.004126594  0.007276051 -0.035724517  0.017602244
## SS  -0.093944326  0.182915243 -0.028449763  0.111744464 -0.086348192
## WM   0.003680308  0.003529975 -0.033968602 -0.105304409 -0.090234294
## LF   0.153230602 -0.009149067  0.022049287 -0.026579679 -0.083294692
## DEF  0.249693636 -0.022242612  0.041302031 -0.077180634 -0.061607019
## BPH  0.013621260  0.036847012 -0.046566055  0.240977822  0.370081726
## WPH  0.084802700 -0.068675537 -0.053314267  0.031664491  0.129589993
## AM  -0.084676095 -0.036706555 -0.021632356 -0.013542181 -0.020150458
## RB  -0.026293083 -0.040152014 -0.022372806 -0.055588817  0.008954225
## RBB -0.060070162 -0.026494705  0.106665543 -0.005931387 -0.004314071
## GLH  0.037758068 -0.024476399 -0.013898620  0.006299050 -0.042530915
## STB -0.046707841 -0.021833211 -0.014435547 -0.005034030 -0.036022872
## BLB -0.052898252  0.244004867 -0.009328385  0.107005210 -0.167556950
## LB  -0.127963391 -0.029882883 -0.038375603  0.107158871 -0.046065138
## BS  -0.148321108  0.234650245 -0.072940695  0.063125234 -0.117485180
## BLS  0.010440521  0.283165176 -0.015161313  0.064805537 -0.091597947
## NBS -0.040997043  0.062340280  0.110136273 -0.073086344 -0.132854423
## RS  -0.061610845  0.134249332 -0.020632530 -0.020909677 -0.051119154
## LS  -0.034450014  0.067177169 -0.015110894  0.080208260 -0.061517557
## SHB  1.000000000 -0.080137282  0.085421207 -0.091517991 -0.068989228
## SHR -0.080137282  1.000000000 -0.010317967 -0.036661984 -0.067508690
## SR   0.085421207 -0.010317967  1.000000000 -0.034332329 -0.039166257
## FSM -0.091517991 -0.036661984 -0.034332329  1.000000000  0.312470581
## NB  -0.068989228 -0.067508690 -0.039166257  0.312470581  1.000000000
## DP  -0.085453830 -0.054508653 -0.053060417 -0.089024740  0.006315548
## RTD -0.045011668  0.025551729 -0.012948416 -0.042180378 -0.010533007
## RSD -0.092223233 -0.050083109 -0.038486830  0.067911292  0.045198423
## RD  -0.014380191 -0.037626438 -0.031749024  0.160507466  0.059221301
##               DP           RTD         RSD            RD
## DH  -0.073634577  0.0280115778 -0.02436441  0.1115033607
## WH  -0.120887145  0.0138276546 -0.05343608  0.0577439830
## SS  -0.053961520 -0.0457348468 -0.06329499 -0.0366457265
## WM   0.149253069  0.1320465279  0.20885537  0.0354742585
## LF  -0.054636388 -0.0287493239  0.08875500  0.0658192011
## DEF  0.034958674  0.2458500795  0.06284876  0.0961617360
## BPH -0.048772243  0.0247196239  0.10680548  0.1025612287
## WPH  0.068384328  0.0309597414  0.10398811  0.0037561741
## AM   0.280690007 -0.0278446566  0.14981460  0.2500272552
## RB   0.277299100  0.0224680413  0.02374587  0.0022877387
## RBB -0.045834422 -0.0208334045 -0.02735287 -0.0268352701
## GLH -0.076978257  0.3551117043  0.14535300  0.1404807312
## STB -0.040783560 -0.0184600247 -0.02858511 -0.0107174763
## BLB -0.103919379 -0.0441366057 -0.09971847 -0.0684203411
## LB   0.190899493 -0.0285283538  0.16344752  0.1109239561
## BS   0.360031613 -0.0153058423  0.11846480 -0.0278337669
## BLS -0.098239821 -0.0451575878 -0.07102290 -0.0242303102
## NBS -0.121521398  0.1546419612 -0.10052277 -0.0144196141
## RS  -0.032796485 -0.0284983206 -0.03677569 -0.0437328291
## LS  -0.027564559  0.0172241058  0.13307465 -0.0110128355
## SHB -0.085453830 -0.0450116675 -0.09222323 -0.0143801911
## SHR -0.054508653  0.0255517285 -0.05008311 -0.0376264377
## SR  -0.053060417 -0.0129484164 -0.03848683 -0.0317490244
## FSM -0.089024740 -0.0421803784  0.06791129  0.1605074655
## NB   0.006315548 -0.0105330067  0.04519842  0.0592213011
## DP   1.000000000  0.1153434724  0.30622162 -0.0271905258
## RTD  0.115343472  1.0000000000  0.04522526  0.0009782336
## RSD  0.306221625  0.0452252648  1.00000000  0.2759749569
## RD  -0.027190526  0.0009782336  0.27597496  1.0000000000
vCorPear <- as.vector(all.pearson)
noO <-vCorPear[!vCorPear %in% 1]
histogram(noO)

# Pearson's correlation coefficents of survey data are around tendding on the negative values.
diag(all.spearman) <- 0

histogram(as.vector(all.spearman))

# the distribution of correlation coefficients are scatttered around the -5 to 5

I chose spearman correlation

all.spearman <- cor(all, method = "spearman", use = "pairwise")
##### network graph #####
InsectInjuiry <- 1:13
Disease <- 14:29
injuries <- list(InsectInjuiry, Disease)
diag(all.spearman) <- 0

from this I would try to use igraph package to visualize the network graph the m

inet <- as.igraph(qnet) # export to igraph object because I use qgraph package to 
adj <- get.adjacency(inet, attr = "weight", sparse = F) # from igraph we need only adjancy matrix
# adj is matrix class
rownames(adj) <- names(all)
colnames(adj) <- names(all)

abadj <- abs(adj)
# plot igraph network model
ig <- graph.adjacency(adj, mode= "undirected", weighted = TRUE, diag=FALSE)
ab.ig <- graph.adjacency(abadj, mode= "undirected", weighted = TRUE, diag=FALSE)

L <- layout.fruchterman.reingold(ig)
plot(ig, layout = L)

cluster_walktrap(ig)
## IGRAPH clustering walktrap, groups: 13, mod: 0.27
## + groups:
##   $`1`
##   [1] "FSM" "NB" 
##   
##   $`2`
##   [1] "LF"  "DEF" "GLH" "SHB" "DP"  "RTD" "RSD" "RD" 
##   
##   $`3`
##   [1] "DH"  "LB"  "BS"  "BLS" "NBS" "RS"  "LS"  "SHR" "SR" 
##   
##   $`4`
##   + ... omitted several groups/vertices
group_wlk <- membership(cluster_walktrap(ig))
group_edge <- membership(cluster_edge_betweenness(ab.ig))
group_greedy <- membership(cluster_fast_greedy(ab.ig))
group_eigen <- membership(cluster_leading_eigen(ab.ig))
group_louvain <- membership(cluster_louvain(ab.ig))
group_optimal <- membership(cluster_optimal(ab.ig))


cluster_optimal(ab.ig)
## IGRAPH clustering optimal, groups: 6, mod: 0.36
## + groups:
##   $`1`
##   [1] "DH"  "WPH" "RBB" "NBS" "SR" 
##   
##   $`2`
##   [1] "WH"  "LF"  "DEF" "GLH" "SHB" "RD" 
##   
##   $`3`
##   [1] "SS"  "WM"  "BPH" "BLB" "BLS" "RS"  "LS"  "SHR"
##   
##   $`4`
##   + ... omitted several groups/vertices
group_wlk
##  DH  WH  SS  WM  LF DEF BPH WPH  AM  RB RBB GLH STB BLB  LB  BS BLS NBS 
##   3   4   5   6   2   2   7   8   9  10  11   2  12  13   3   3   3   3 
##  RS  LS SHB SHR  SR FSM  NB  DP RTD RSD  RD 
##   3   3   2   3   3   1   1   2   2   2   2
group_edge
##  DH  WH  SS  WM  LF DEF BPH WPH  AM  RB RBB GLH STB BLB  LB  BS BLS NBS 
##   1   2   3   3   4   5   3   6   7   7   8   5   9   3   3  10   3  11 
##  RS  LS SHB SHR  SR FSM  NB  DP RTD RSD  RD 
##   3   3   4   3   8  12  12  13   7   7   5
group_greedy
##  DH  WH  SS  WM  LF DEF BPH WPH  AM  RB RBB GLH STB BLB  LB  BS BLS NBS 
##   2   1   4   4   1   1   2   2   3   3   2   1   6   4   3   3   4   2 
##  RS  LS SHB SHR  SR FSM  NB  DP RTD RSD  RD 
##   4   4   1   4   2   5   5   3   3   3   1
group_eigen
##  DH  WH  SS  WM  LF DEF BPH WPH  AM  RB RBB GLH STB BLB  LB  BS BLS NBS 
##   1   3   4   4   3   3   1   1   5   5   5   3   2   4   5   5   4   1 
##  RS  LS SHB SHR  SR FSM  NB  DP RTD RSD  RD 
##   4   4   3   4   1   6   4   5   5   5   3
group_louvain
##  DH  WH  SS  WM  LF DEF BPH WPH  AM  RB RBB GLH STB BLB  LB  BS BLS NBS 
##   4   5   2   2   6   6   2   4   5   5   5   6   1   2   5   5   2   4 
##  RS  LS SHB SHR  SR FSM  NB  DP RTD RSD  RD 
##   2   2   6   2   4   3   3   5   5   5   6
group_optimal
##  DH  WH  SS  WM  LF DEF BPH WPH  AM  RB RBB GLH STB BLB  LB  BS BLS NBS 
##   1   2   3   3   2   2   3   1   4   4   1   2   5   3   4   4   3   1 
##  RS  LS SHB SHR  SR FSM  NB  DP RTD RSD  RD 
##   3   3   2   3   1   6   6   4   4   4   2
#par(mfrow=c(4,2))

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        color = group_wlk, 
        title = "walk trap",
        vsize = 5, 
        line = 3,
        legend = FALSE,
        vTrans = 200
)

Walk trap comminity

Blue group: Stink bug:STB and WH: white head

G1: bacterial leaf blight and silver shoot are in the same group as well

Grey group: whorl maggot

Black group: amry worm and flase smut and neck blast in the group

Green group leaf scald, ragged stunt, sheath rot, bacterial leaf streak, leaf blase, brown spot, rice black bug, sheath rod, dead heart

Pink group: white backed plant hopper

Yellow group: brown plant hopper

Red group: rice tungro disease, dirty panicle ragged stunt disease, leaf folder, sheathblight defoliators, green leaf hopper, rat damage

1 fsmx nbx 2 lfa defa glhx shbx dpx rtdx rtx 3 dhx lba bsa blsa nbsa rsa lsa shrx srx 4 whx 5 ssx 6 wma 7 bphx 8 wbphx 9 awx 10 rbx 11 rbbx 12 stbx 13 blba

dhx whx ssx wma lfa defa bphx wbpx awx rbx rbbx glhx stbx blba lba 3 4 5 6 2 2 7 8 9 10 11 2 12 13 3 bsa blsa nbsa rsa lsa shbx shrx srx fsmx nbx dpx rtdx rsdx rtx 3 3 3 3 3 2 3 3 1 1 2 2 2 2

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        color = group_edge, 
        title = "group_edge",
        line = 3,
        legend = TRUE,
       nodeNames = Names,
        legend.cex = 0.3,
        vTrans = 200
)

G1: STB DHX because there is zero and one connects to other variables

G2: FSM, NBX there are close heach other

G3: DPX, DEF, GLH, RTX I have no idea why dpx included in the group

G1: dhx

G2: whx

G3: ssx wma bphx blba lba bsa blsa rsa lsa shrx

G4: lfa shbx

G5: defa glhx rtx

G6: wbph

G7: awx rtdx rsdx

G8: rbbx srz

G9: stbx

G10: bsa

G11: nbsa

G12: fsmx nbx

G13 dpx

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        color = group_greedy, 
        title = "group_greedy",
        vsize = 5, 
        line = 3,
        legend = TRUE,
       nodeNames = Names,
        legend.cex = 0.3,
        vTrans = 200
)

G1: whx lfa defa glhx shbx rtx

G2: dhx bphx wbpx rbbx nbsa srx

G3 awx rbx lba bsa dpx rtdx rsdx

G4: ssx wma blba blsa rsa lsa shrx

G5: fsmx nbx

G6: stbx

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        color = group_eigen, 
        title = "group_eigen",
        vsize = 5, 
        line = 3,
        legend = TRUE,
       nodeNames = Names,
        legend.cex = 0.3,
        vTrans = 200
)

Inset the description here for engin comminity

Red: Stink bug

Pink: False smut

Blue: Lead scald, red stripe, sheath rot, bacterial leaf strak, whorl maggot, silver shoot, bacterial leaf blight

Black: WBPH, BPH, NBS, Stem Rot, DH

light blue: RTD, AW, DPX, RBX

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        color = group_louvain, 
        title = "group_louvain",
        vsize = 5, 
        line = 3,
        legend = TRUE,
       nodeNames = Names,
        legend.cex = 0.3,
        vTrans = 200
)

Inset the description here for louvanin comminity I like this grop communities personally with some reasons

Red: RSA, WMA, BLS, BLB, SSX, SHR, LSA, BPH

Blue: WBPH, NBS, SRX, DHX

Pink: LFA, SHB, DEF, RTX, GLH

lingt blue:TRD, AWX, RSD, DPX, WHX, BSA, LBA, WHX(8)

Green: false smut, NBX (2)

Black: Stink bug (1)

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        color = group_optimal, 
        title = "group_optimal",
        vsize = 5, 
        line = 3,
        legend = TRUE,
       nodeNames = Names,
        legend.cex = 0.3,
        vTrans = 200
)

communities of the function of fast greedy

Clustering by the Eigen clustering showed that

Class one (red) -> Leaffolder, shealth blight, defoliators, rat damages, green leaf hopper, and whitehead (6)

Class two (blue) -> rice tugro disease, rice bug, dirty panicle, brown spot ragged stund disease, army worm, leaf blast, and rice black bug

Class three (black) -> brown plant hopper , white backed plant hopper, neck blast, stem rot, deadheart (5)

Class four (green) -> whorl maggot, bacterial leaf streak, sheath rot, leaf scald, silver shoot, bacterial leaf blight, narrow brown spot (7)

Class five (pink) -> False smut and neck blast (2)

Class six -> Stink bug (1)

qgraph(all.spearman, 
        layout = "spring", 
        threshold = 0.20,
        maximum = 1,
        group = group_eigen,
      # node.color = brewer.pal(7,"Set2"),
        title = "group_eigen",
        vsize = 5, 
        line = 3,
        legend = TRUE,
        nodeNames = Names,
        legend.cex = 0.3,
        vTrans = 200
)

From this, there are 5 groups.

Red group: Stink bug (1)

Pink group: false smut (1)

Blue group: NBX, LSA, RSA, SHR, SSX, BLB, SSX, BLS, WMA (8)

light blue group: RBX, RTD, DPX, BSA, RSD, LBA, AWX (7)

Black group: WBPH, BPH, NBS, SRX, DHX (5)

Considering the comminity by edges betweenness fuctionc. I think that the communites are not make senses. hight betweenness in one group and low betweenness are in other group.

# compute the topological properties
ppi <- 300
png("centering.png", width = 9*ppi, height = 6*ppi, res = ppi)
cen <- centrality_auto(qnet)

topo <- cen$node.centrality
topo$node <- row.names(topo)
row.names(topo) <- NULL
topo.melt <- melt(topo)
## Using node as id variables
p1 <- topo %>% ggplot(aes(x= Strength, y = reorder(node, Strength))) +
         geom_point(size = 3, color ="red") +
        theme_bw() +
        theme(panel.grid.major.x =  element_blank(),
              panel.grid.minor.x =  element_blank(),
              panel.grid.major.y = element_line(color = "grey", linetype = 3)) +
        xlab("Node degree") + 
        ylab("Variables")  

p2 <- topo %>% ggplot(aes(x= Betweenness, y = reorder(node, Betweenness))) + 
        geom_point(size = 3, color ="blue") +
        theme_bw() +
        theme(panel.grid.major.x =  element_blank(),
              panel.grid.minor.x =  element_blank(),
              panel.grid.major.y = element_line(color = "grey", linetype = 3)) +
        xlab("Betweenness") + 
        ylab("Variables")  

# Compute clustering coefficients

clust <- clustcoef_auto(qnet)
clusZ.sp <- NULL
clusZ.sp$node <- rownames(clust)
clusZ.sp$clustZhang <- clust$clustZhang
clusZ.sp <- as.data.frame(clusZ.sp)

p3 <- clusZ.sp %>% 
        ggplot(aes(x= clustZhang, y = reorder(node, clustZhang))) +
        geom_point(size = 3, color ="yellow") + 
        theme_bw() +
        theme(panel.grid.major.x =  element_blank(),
              panel.grid.minor.x =  element_blank(),
              panel.grid.major.y = element_line(color = "grey", linetype = 3)) +
        xlab("Clustering coefficient") + 
        ylab("Variables")

plot_grid(p1, p2, p3, labels=c("A", "B", "C"), ncol = 3, nrow = 1 )
dev.off()
## quartz_off_screen 
##                 2

This figure reveals that most cognitive depressive symptoms (e.g., ‘‘feeling sad’’ (sad), ‘‘feeling irritable’’ (irr), ‘‘quality of mood’’ (qmo), ‘‘response of your mood to good or desired events’’ (rmo), ‘‘concentration problems’’ (con), and ‘‘self criticism and blame’’ (sel)) seem to be clustered together.

These symptoms also seem to score moderate to high on at least two out of three centrality measures (Figure 4). For example, ‘‘rmo’’ has a moderate strength and a very high clustering coefficient, whereas it has a low betweenness. This indicates that activation in the network does not easily affect response of mood to positive events (low betweenness), but that,

if the symptom is activated, the cluster will tend to stay infected because of the high interconnectivity (high clustering coefficient).

Another interesting example is ‘‘energy level’’ (ene), which has a high node strength and betweenness, but a moderate clustering coefficient.

Apparently, energy level has many and/or strong connections (high strength) and lies on many paths between symptoms (high betweenness), whereas it is not part of a strongly clustered group of symptoms (moderate clustering coefficient). This symptom is probably more important in passing information through the network, or between other clusters, and might, therefore, be an interesting target for intervention.

Which nodes have high strength (node degree) between the node which have high betweenness

As opposed to cognitive depressive symptoms, most anxiety and somatic symptoms (e.g., ‘‘panic/phobic symptoms’’ (pan), ‘‘aches and pains’’ (ach), ‘‘psychomotor agitation’’ (agi)) feature low scores on at least two centrality measures. Apparently, most anxiety and somatic symptoms either are less easily affected by other activated symptoms, do not tend to stay infected because of low interconnectivity (low clustering coefficient), or are less important for transfer- ring information through the network (low betweenness). This is to be expected, since participants with a current or history of anxiety disorder are excluded from our sample. The item ‘‘feeling anxious’’ (anx), however, seems to be an important exception; feeling anxious does have a high node strength, a relatively high betweenness, and a moderate clustering coefficient. Apparently, feeling anxious does play an important role in our sample of depressive and healthy persons: it can be activated very easily, since a lot of information flows through it (high betweenness), and, in turn, it can activate many other symptoms because it has many neighbours (high node strength, moderate clustering). The role of feeling anxious in our network is in line with high comorbidity levels of anxiety and

## Gene Significant mu

# verboseScatterplot(abs(geneModuleMembership[moduleGenes, column]),
#                    abs(geneTraitSignificance[moduleGenes, 1]),
#                    xlab = paste("Module Membership in", module, "module"),
#                    ylab = "Gene significance for body weight",
#                    main = paste("Module membership vs. gene significance\n"),
#                    cex.main = 1.2, cex.lab = 1.2, cex.axis = 1.2, col = module)

why? the flase smut and neck blast

rice bug or stink bug control in the

I use